{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3382b557afd6bf5ef06125442fae7ebc393ba81b"
   },
   "source": [
    "# ML or Data Science Basic Packages(Day-03)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_kg_hide-input": true,
    "_uuid": "4cbd702b7b9bfb81f55636c68de86965bbf98e96"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "@import url('https://fonts.googleapis.com/css?family=Ewert|Roboto&effect=3d|ice|');\n",
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       "a {color: #000000; font-family: 'Roboto';} \n",
       "h1 {color: #000000; font-family: 'Orbitron'; text-shadow: 4px 4px 4px #aaa;} \n",
       "h2, h3 {color: slategray; font-family: 'Orbitron'; text-shadow: 4px 4px 4px #aaa;}\n",
       "h4 {color: #818286; font-family: 'Roboto';}\n",
       "span {font-family:'Roboto'; color:black; text-shadow: 5px 5px 5px #aaa;}  \n",
       "div.output_area pre{font-family:'Roboto'; font-size:110%; color:lightblue;}      \n",
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       "<IPython.core.display.HTML object>"
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   "source": [
    "%%html\n",
    "<style>\n",
    "@import url('https://fonts.googleapis.com/css?family=Ewert|Roboto&effect=3d|ice|');\n",
    "body {background-color: gainsboro;} \n",
    "a {color: #000000; font-family: 'Roboto';} \n",
    "h1 {color: #000000; font-family: 'Orbitron'; text-shadow: 4px 4px 4px #aaa;} \n",
    "h2, h3 {color: slategray; font-family: 'Orbitron'; text-shadow: 4px 4px 4px #aaa;}\n",
    "h4 {color: #818286; font-family: 'Roboto';}\n",
    "span {font-family:'Roboto'; color:black; text-shadow: 5px 5px 5px #aaa;}  \n",
    "div.output_area pre{font-family:'Roboto'; font-size:110%; color:lightblue;}      \n",
    "</style>"
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  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "74bf89918d6ac9892cf38b1c42fd15a7faab1f72"
   },
   "source": [
    "# Intro of this notebook:\n",
    "In this notebook we are going to get familiar with Python. In Day-03 we will cover the ML or Data Science basic packages portion of Python which can be very useful for Machine Learning or Data Science.\n",
    "![Imgur](https://i.imgur.com/oNi7u0g.jpg)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1fba80df1fb488dd42938a53aee0250aa5555392"
   },
   "source": [
    "## ML or Data Science Packages topics:\n",
    "we will cover the ML or Data Science basic packages portion of Python.ML or Data Science packages parts below here —\n",
    "\n",
    "1. Numpy\n",
    "\n",
    "2. Pandas\n",
    "\n",
    "3. Matplotlib\n",
    "\n",
    "4. Seaborn\n",
    "\n",
    "5. Scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "825e4acdd3f90ee38b64eb3471c375a0a1c0787c"
   },
   "source": [
    "## 1. Numpy\n",
    "![Imgur](https://i.imgur.com/yJn0MEC.png)\n",
    "NumPy is the fundamental package for scientific computing with Python. It contains among other things:\n",
    "\n",
    "* a powerful N-dimensional array object\n",
    "\n",
    "* sophisticated (broadcasting) functions\n",
    "\n",
    "* tools for integrating C/C++ and Fortran code\n",
    "\n",
    "* useful linear algebra, Fourier transform, and random number capabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "4ecf4ffc678363ceba7130fa2c8db56f03166d21"
   },
   "source": [
    "> Everyone can learn [Numpy](https://hackernoon.com/numpy-with-python-for-data-science-16ff2f646591) from my article.click on [\"Numpy\"](https://hackernoon.com/numpy-with-python-for-data-science-16ff2f646591) then you can see this article."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8fb0962383666857c3cbf124efdafc37bac147f6"
   },
   "source": [
    "## 2. Pandas\n",
    "![Imgur](https://i.imgur.com/Vy0rMEr.png)\n",
    "Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.\n",
    "\n",
    "Pandas is a NumFOCUS sponsored project. This will help ensure the success of development of pandas as a world-class open-source project, and makes it possible to donate to the project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "487a6a1b019200068970ddcc1d09f16d096fdb6f"
   },
   "source": [
    "> Everyone can learn [Pandas](https://www.kaggle.com/harunshimanto/pandas-with-data-science-ai) from my kernel.click on [\"Pandas\"](https://www.kaggle.com/harunshimanto/pandas-with-data-science-ai) then you can see this kernel.\n",
    "\n",
    "![Imgur](https://i.imgur.com/sO3hkdl.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6dca4bb57a6f344303360f2376b2236a0355755c"
   },
   "source": [
    "## 3. Matplotlib\n",
    "Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.\n",
    "![Imgur](https://i.imgur.com/ix4izp4.png)\n",
    "> Everyone can learn [Matplotlib](https://www.kaggle.com/harunshimanto/matplotlib-with-data-science-ai) from my kernel also.click on [\"Matplotlib\"](https://www.kaggle.com/harunshimanto/matplotlib-with-data-science-ai) then you can see this kernel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e9f9b98c183f40ad4180fb4fdbe46f5fb6474fa8"
   },
   "source": [
    "### Data Visualisation is an important aspect of data science. Why? \n",
    "Because data scientists deal with humongous amounts of data. And it becomes easier to understand the data if we can see it visually and dig deeper.\n",
    "For this sole purpose, Python has the matplotlib package to generate simple and interactive visualisations. Let's start using matplotlib to extract information from data. \n",
    "For this purpose, We will be using the [WHO_SUICIDE_STATISTICS dataset](https://www.kaggle.com/szamil/who-suicide-statistics) that captures the number of suicides per age group of several countries over 1990-2017. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "af472c176de52c3fc6feb169b479a25f51c3916b"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "data= pd.read_csv(\"../input/who_suicide_statistics.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "e7e405ecc7f507ad5ed72cf0d480b29a2fb287b8",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>277900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>246800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>267500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>5-14 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>298300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>138700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>301400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>264200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>296700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>5-14 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>325800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year     sex          age  suicides_no  population\n",
       "0  Albania  1985  female  15-24 years          NaN    277900.0\n",
       "1  Albania  1985  female  25-34 years          NaN    246800.0\n",
       "2  Albania  1985  female  35-54 years          NaN    267500.0\n",
       "3  Albania  1985  female   5-14 years          NaN    298300.0\n",
       "4  Albania  1985  female  55-74 years          NaN    138700.0\n",
       "5  Albania  1985  female    75+ years          NaN     34200.0\n",
       "6  Albania  1985    male  15-24 years          NaN    301400.0\n",
       "7  Albania  1985    male  25-34 years          NaN    264200.0\n",
       "8  Albania  1985    male  35-54 years          NaN    296700.0\n",
       "9  Albania  1985    male   5-14 years          NaN    325800.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "95f4c1d72be4efc205a5b2dec94427799d789f2a",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country         object\n",
       "year             int64\n",
       "sex             object\n",
       "age             object\n",
       "suicides_no    float64\n",
       "population     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "46c70fd492104910cbfb994348b4c81c23f76ff3"
   },
   "source": [
    "Here one important fallacy is that the year column in an integer. but it is supposed to be a date-time column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "0ea9a5045b77164c268a7b72c043c68be75f36ea"
   },
   "outputs": [],
   "source": [
    "data.year=pd.to_datetime(data.year,format=\"%Y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "029f4b59b76ca06c95d0d603ed5ed9df2e54de60"
   },
   "source": [
    "We can clearly see how the data is stored. First the country column stores the country name then the year column stores the year number which is repeated for gender then age-groups.\n",
    "First let's analyse the data for the country 'Albania'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "53afc37d01add52a5986073f7cba91997c4c4e91"
   },
   "outputs": [],
   "source": [
    "albania_data=data[data['country']=='Albania']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e8b05a181d85f00035c4a57128a3ca8e2ea68c68"
   },
   "source": [
    "Lets drop the country column as it doesn't make sense to have same value repeated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_kg_hide-input": true,
    "_kg_hide-output": true,
    "_uuid": "4959da3c28d92d05e4b48fe9df0949f4b3f737c8",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "albania_data=albania_data.drop('country',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6763f732741b63f6da4f7d254731a86b0004026e"
   },
   "source": [
    "We begin by seeing the range of the population column with respect to time. With this information we can disclose how the population of Albania shifted ove the years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6f1e281a649cda2ae1e22372d2ccdfead34f3a39"
   },
   "source": [
    "We begin by skimming through the female population for each age_group and plotting them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "3a9374768cc8808068a0529d2a3d9b9011cdbcd4"
   },
   "outputs": [],
   "source": [
    "f_albania = albania_data[albania_data['sex']=='female']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "a878dc43e8106f0455a3232aa5e2c1d3b400343b"
   },
   "outputs": [],
   "source": [
    "table=pd.pivot_table(f_albania,index=\"year\",columns=\"age\",values=\"population\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "db2e37e007677c955c690507c3275c40a206c26c",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>age</th>\n",
       "      <th>15-24 years</th>\n",
       "      <th>25-34 years</th>\n",
       "      <th>35-54 years</th>\n",
       "      <th>5-14 years</th>\n",
       "      <th>55-74 years</th>\n",
       "      <th>75+ years</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1985-01-01</th>\n",
       "      <td>277900.0</td>\n",
       "      <td>246800.0</td>\n",
       "      <td>267500.0</td>\n",
       "      <td>298300.0</td>\n",
       "      <td>138700.0</td>\n",
       "      <td>34200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-01</th>\n",
       "      <td>283900.0</td>\n",
       "      <td>252100.0</td>\n",
       "      <td>273200.0</td>\n",
       "      <td>304700.0</td>\n",
       "      <td>141700.0</td>\n",
       "      <td>34900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-01-01</th>\n",
       "      <td>289700.0</td>\n",
       "      <td>257200.0</td>\n",
       "      <td>278800.0</td>\n",
       "      <td>311000.0</td>\n",
       "      <td>144600.0</td>\n",
       "      <td>35600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-01</th>\n",
       "      <td>295600.0</td>\n",
       "      <td>262400.0</td>\n",
       "      <td>284500.0</td>\n",
       "      <td>317200.0</td>\n",
       "      <td>147500.0</td>\n",
       "      <td>36400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-01-01</th>\n",
       "      <td>299900.0</td>\n",
       "      <td>266300.0</td>\n",
       "      <td>288600.0</td>\n",
       "      <td>321900.0</td>\n",
       "      <td>149600.0</td>\n",
       "      <td>37000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>305900.0</td>\n",
       "      <td>278000.0</td>\n",
       "      <td>308300.0</td>\n",
       "      <td>329600.0</td>\n",
       "      <td>156400.0</td>\n",
       "      <td>37600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-01-01</th>\n",
       "      <td>301100.0</td>\n",
       "      <td>274700.0</td>\n",
       "      <td>316500.0</td>\n",
       "      <td>333300.0</td>\n",
       "      <td>160800.0</td>\n",
       "      <td>38300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>292400.0</td>\n",
       "      <td>267400.0</td>\n",
       "      <td>323100.0</td>\n",
       "      <td>336700.0</td>\n",
       "      <td>164900.0</td>\n",
       "      <td>38700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-01-01</th>\n",
       "      <td>285300.0</td>\n",
       "      <td>261800.0</td>\n",
       "      <td>331200.0</td>\n",
       "      <td>340300.0</td>\n",
       "      <td>169500.0</td>\n",
       "      <td>39300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-01</th>\n",
       "      <td>282600.0</td>\n",
       "      <td>261100.0</td>\n",
       "      <td>342500.0</td>\n",
       "      <td>344400.0</td>\n",
       "      <td>174600.0</td>\n",
       "      <td>39900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>283500.0</td>\n",
       "      <td>264000.0</td>\n",
       "      <td>356400.0</td>\n",
       "      <td>348700.0</td>\n",
       "      <td>180400.0</td>\n",
       "      <td>40800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-01-01</th>\n",
       "      <td>287700.0</td>\n",
       "      <td>267900.0</td>\n",
       "      <td>362000.0</td>\n",
       "      <td>354100.0</td>\n",
       "      <td>183100.0</td>\n",
       "      <td>41200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-01-01</th>\n",
       "      <td>294000.0</td>\n",
       "      <td>273900.0</td>\n",
       "      <td>370100.0</td>\n",
       "      <td>361800.0</td>\n",
       "      <td>187000.0</td>\n",
       "      <td>42100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01</th>\n",
       "      <td>295600.0</td>\n",
       "      <td>275300.0</td>\n",
       "      <td>372100.0</td>\n",
       "      <td>363800.0</td>\n",
       "      <td>188200.0</td>\n",
       "      <td>42300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01</th>\n",
       "      <td>296800.0</td>\n",
       "      <td>276500.0</td>\n",
       "      <td>373600.0</td>\n",
       "      <td>365200.0</td>\n",
       "      <td>188800.0</td>\n",
       "      <td>42400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>263900.0</td>\n",
       "      <td>245800.0</td>\n",
       "      <td>332200.0</td>\n",
       "      <td>324700.0</td>\n",
       "      <td>168000.0</td>\n",
       "      <td>37800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>271359.0</td>\n",
       "      <td>222771.0</td>\n",
       "      <td>370191.0</td>\n",
       "      <td>307356.0</td>\n",
       "      <td>189799.0</td>\n",
       "      <td>47254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01</th>\n",
       "      <td>275970.0</td>\n",
       "      <td>223685.0</td>\n",
       "      <td>375113.0</td>\n",
       "      <td>304850.0</td>\n",
       "      <td>191712.0</td>\n",
       "      <td>47407.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-01</th>\n",
       "      <td>283709.0</td>\n",
       "      <td>222941.0</td>\n",
       "      <td>381760.0</td>\n",
       "      <td>298477.0</td>\n",
       "      <td>195699.0</td>\n",
       "      <td>49088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01</th>\n",
       "      <td>292268.0</td>\n",
       "      <td>222389.0</td>\n",
       "      <td>391436.0</td>\n",
       "      <td>286705.0</td>\n",
       "      <td>203841.0</td>\n",
       "      <td>50970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>281922.0</td>\n",
       "      <td>190745.0</td>\n",
       "      <td>386513.0</td>\n",
       "      <td>276559.0</td>\n",
       "      <td>210998.0</td>\n",
       "      <td>53191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>283138.0</td>\n",
       "      <td>186391.0</td>\n",
       "      <td>388746.0</td>\n",
       "      <td>267316.0</td>\n",
       "      <td>215907.0</td>\n",
       "      <td>56011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>281080.0</td>\n",
       "      <td>183629.0</td>\n",
       "      <td>391811.0</td>\n",
       "      <td>256808.0</td>\n",
       "      <td>221120.0</td>\n",
       "      <td>57404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>276073.0</td>\n",
       "      <td>182663.0</td>\n",
       "      <td>393832.0</td>\n",
       "      <td>246288.0</td>\n",
       "      <td>226448.0</td>\n",
       "      <td>59369.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>270003.0</td>\n",
       "      <td>182712.0</td>\n",
       "      <td>394286.0</td>\n",
       "      <td>236174.0</td>\n",
       "      <td>233302.0</td>\n",
       "      <td>61582.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>263581.0</td>\n",
       "      <td>183579.0</td>\n",
       "      <td>394593.0</td>\n",
       "      <td>223969.0</td>\n",
       "      <td>241491.0</td>\n",
       "      <td>64415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>258006.0</td>\n",
       "      <td>184556.0</td>\n",
       "      <td>394638.0</td>\n",
       "      <td>210822.0</td>\n",
       "      <td>250641.0</td>\n",
       "      <td>67649.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>249730.0</td>\n",
       "      <td>187346.0</td>\n",
       "      <td>395819.0</td>\n",
       "      <td>198385.0</td>\n",
       "      <td>259751.0</td>\n",
       "      <td>69811.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>237550.0</td>\n",
       "      <td>191853.0</td>\n",
       "      <td>397517.0</td>\n",
       "      <td>187558.0</td>\n",
       "      <td>269026.0</td>\n",
       "      <td>71601.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>224883.0</td>\n",
       "      <td>195460.0</td>\n",
       "      <td>399197.0</td>\n",
       "      <td>177838.0</td>\n",
       "      <td>278887.0</td>\n",
       "      <td>73945.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>212324.0</td>\n",
       "      <td>199024.0</td>\n",
       "      <td>400275.0</td>\n",
       "      <td>168140.0</td>\n",
       "      <td>289055.0</td>\n",
       "      <td>76861.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "age         15-24 years  25-34 years    ...      55-74 years  75+ years\n",
       "year                                    ...                            \n",
       "1985-01-01     277900.0     246800.0    ...         138700.0    34200.0\n",
       "1986-01-01     283900.0     252100.0    ...         141700.0    34900.0\n",
       "1987-01-01     289700.0     257200.0    ...         144600.0    35600.0\n",
       "1988-01-01     295600.0     262400.0    ...         147500.0    36400.0\n",
       "1989-01-01     299900.0     266300.0    ...         149600.0    37000.0\n",
       "1990-01-01     305900.0     278000.0    ...         156400.0    37600.0\n",
       "1991-01-01     301100.0     274700.0    ...         160800.0    38300.0\n",
       "1992-01-01     292400.0     267400.0    ...         164900.0    38700.0\n",
       "1993-01-01     285300.0     261800.0    ...         169500.0    39300.0\n",
       "1994-01-01     282600.0     261100.0    ...         174600.0    39900.0\n",
       "1995-01-01     283500.0     264000.0    ...         180400.0    40800.0\n",
       "1996-01-01     287700.0     267900.0    ...         183100.0    41200.0\n",
       "1997-01-01     294000.0     273900.0    ...         187000.0    42100.0\n",
       "1998-01-01     295600.0     275300.0    ...         188200.0    42300.0\n",
       "1999-01-01     296800.0     276500.0    ...         188800.0    42400.0\n",
       "2000-01-01     263900.0     245800.0    ...         168000.0    37800.0\n",
       "2001-01-01     271359.0     222771.0    ...         189799.0    47254.0\n",
       "2002-01-01     275970.0     223685.0    ...         191712.0    47407.0\n",
       "2003-01-01     283709.0     222941.0    ...         195699.0    49088.0\n",
       "2004-01-01     292268.0     222389.0    ...         203841.0    50970.0\n",
       "2005-01-01     281922.0     190745.0    ...         210998.0    53191.0\n",
       "2006-01-01     283138.0     186391.0    ...         215907.0    56011.0\n",
       "2007-01-01     281080.0     183629.0    ...         221120.0    57404.0\n",
       "2008-01-01     276073.0     182663.0    ...         226448.0    59369.0\n",
       "2009-01-01     270003.0     182712.0    ...         233302.0    61582.0\n",
       "2010-01-01     263581.0     183579.0    ...         241491.0    64415.0\n",
       "2011-01-01     258006.0     184556.0    ...         250641.0    67649.0\n",
       "2012-01-01     249730.0     187346.0    ...         259751.0    69811.0\n",
       "2013-01-01     237550.0     191853.0    ...         269026.0    71601.0\n",
       "2014-01-01     224883.0     195460.0    ...         278887.0    73945.0\n",
       "2015-01-01     212324.0     199024.0    ...         289055.0    76861.0\n",
       "\n",
       "[31 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_uuid": "ec5ac68d499a532a0265ccbc747f2a734b5ab57a",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [16,9]\n",
    "plt.plot(table)\n",
    "plt.legend(table.columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "0fb5a8f5af09003d79cbb6468e81225cf0bf79b4"
   },
   "source": [
    "Woah! That is some serious ups and downs in the population right there. Do you notice the maroon line i.e 5-14 years column that decreases so steeply. Does it mean female child killing?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c1da669a942e2c248b5c4bf764a4ed8cc083cfcd"
   },
   "source": [
    "Let's go ahead with a pie chart now, For this purpose we are building a piechart for both the female and male population of canada for the year of 2013"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "352f77c0f26ae7d2dcf8104b9663a2dce50e49c9"
   },
   "source": [
    "## Pie Chart "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "b10149837a52a6c8e800d6afcc89b25cf42a5de1",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "canada_data=data[data['country']=='Canada']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "ba751ec05eef588aaaa678bc76818ba5aaecc915"
   },
   "outputs": [],
   "source": [
    "pie_data = canada_data[canada_data['year']=='2013-01-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "d7edf72dacbc23a77dddcd8f9ace8aa2560cffb4"
   },
   "outputs": [],
   "source": [
    "pie_female = pie_data[pie_data['sex']=='female']\n",
    "pie_male = pie_data[pie_data['sex']=='male']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "62351dff59a48f72932b0bcfbab9ad0407a4eb98"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [8,12]\n",
    "plt.subplot(211)\n",
    "plt.title('Number of suicides for each age-group(CANADIAN MALE)')\n",
    "plt.pie(pie_male.suicides_no,labels = pie_male.age)\n",
    "plt.subplot(212)\n",
    "plt.title('Number of suicides for each age-group(CANADIAN FEMALE)')\n",
    "plt.pie(pie_female.suicides_no,labels = pie_female.age)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "25fee87a3a09974f31a0b16ba028f2ee93c134c4"
   },
   "source": [
    "The same data can be used for plotting a bar chart as follows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "0773c0d27f8452c2282445499294bde1228095df"
   },
   "source": [
    "## Bar Chart "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "ddf7ccbfa82d20c59add14b0d2b651eae4fe9f9c",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(pie_female.age,height=pie_female['suicides_no'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "af408f00f2ab92bc8b0a83dbebfb7356ac899cbe"
   },
   "source": [
    "## Histogram Chart\n",
    "Next we plan to build a histogram. For this purpose we are using the population where it is greater than 1500000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "e60826ca242852e86b05ba2c0727e9012c476a98"
   },
   "outputs": [],
   "source": [
    "x=data[data['population'] >= 1500000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "1791690fc4f9cc5defe78af760daf2182e1520e1",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "hist_data = x.population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "a07eaa7ac765323ca33a61ad61e5ead725443b74"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(hist_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "61ab7ff93b7dd4a4402e1001082bb55755c8bffc"
   },
   "source": [
    "## 4.Seaborn\n",
    "Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.\n",
    "![Imgur](https://i.imgur.com/4IBZrCS.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "1e046bcd64e6b148e5c5a54329d92ae730580326"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "90f4ef5143fa1011d978b9bd3c2bc171911d9aa7"
   },
   "source": [
    "### 4.1 Count Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "bcd1bbdf88f78bba3231192ce47371bb0e49bc32",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2643ef83c8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "sns.countplot(data = data,x=data.country)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "395c05627bfdc3cd8c23b2c3fa69c3f794dc31fc"
   },
   "source": [
    "### 4.2 KDE Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "7b9c92d030a3228573d51495ef6a7477c377411c",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np; np.random.seed(10)\n",
    "import seaborn as sns; sns.set(color_codes=True)\n",
    "mean, cov = [0, 2], [(1, .5), (.5, 1)]\n",
    "x, y = np.random.multivariate_normal(mean, cov, size=50).T\n",
    "ax = sns.kdeplot(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e4fac601095bdb96e8e60db98ca2d52dde66946b"
   },
   "source": [
    "### 4.3 DistPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "dc06d8247dbd38810ea08ccde6361d2938dee1ff",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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ejvXgho3x2rVrtXnzZi1cuFCStHv3bn3jG9/Qf/zHf+j0009P60VaWjrkOO6RjXQwgYDaO3qGfLirK6amRGL0X3ccikaL1dTUbnoY4wbHO3s41tkzHo61bVsjmoAOG+PLLrtMl112Werjuro6LV++XLW1tRm/GAAAOBTvMwYAwLBhZ8YHW7NmzViMAwCAcYuZMQAAhhFjAAAMI8YAABhGjAEAMIwYAwBgGDEGAMAwYgwAgGHEGAAAw4gxAACGEWMAAAwjxgAAGEaMAQAwjBgDAGAYMQYAwDBiDACAYcQYAADDiDEAAIYRYwAADCPGAAAYRowBADCMGAMAYBgxBgDAMGIMAIBhxBgAAMOIMQAAhhFjAAAMI8YAABhGjAEAMIwYAwBgGDEGAMAwYgwAgGHEGAAAw4gxAACGEWMAAAwjxgAAGEaMAQAwjBgDAGAYMQYAwDBiDACAYcQYAADDiDEAAIYRYwAADCPGAAAYRowBADCMGAMAYBgxBgDAsJyMcWtHTK7rmh4GAABpybkYt3X26umXP1R9S5fpoQAAkJaci3FnT58kKdabMDwSAADSk3MxjvU5kiSHZWoAgE/kXoz7Z8SOQ4wBAP6QezHu648xM2MAgE/kXoxTM2PDAwEAIE25F2NmxgAAnyHGAAAYlnsx7l+mdrmACwDgE7kX4/6ZcYIWAwB8IvdizFubAAA+k1MxdhxXvfHkZdTsTQ0A8IucivHAErXEzBgA4B+5G2NmxgAAn8jhGBscCAAAGcitGPeyTA0A8J/cinH/HZtsyyLGAADfyK0Y98YlSQV5Ac4ZAwB8I7di3OfItiyFQwHOGQMAfCPHYpxQXtiWbVlshwkA8I3cinFvQnmhgGybtzYBAPwjaHoAoyk5Mw5ILldTAwD8IydnxpZtMTMGAPhGWjPjK6+8Ujt27JBt24pEIvr3f/93zZw5c6zHlrFYXzLGiYSr/nc5AQDgeWnFeOnSpSouLpYk/f73v9f111+vJ598ckwHlinXdVPL1N29CWbGAADfSGuZeiDEktTR0SHLssZsQCPVF3fkulJ+KCDb4gIuAIB/pH0B1/e//3298sorcl1XDz/88FiOaUQG9qXOCwfYgQsA4Ctpx/i2226TJD311FO644479NBDD6X9IpWVRZmPLA2Ne7pUXJQvSerqTZ4kLisuUHNrTJKlSCRP0YrImLz2eBSNFg//RRg1HO/s4VhnD8d6cBm/tencc8/VD37wA+3du1fl5eVpPaelpWNsZqqBgNo7eiRJe1u7JElOIqFEwlE84airK6amROJw3wFpikaL1dTUbnoY4wbHO3s41tkzHo61bVsjmoAOe864s7NT9fX1qY/XrFmj0tJSlZWVZfxiY6mnd79lajt5QRcAAH4w7My4u7tb11xzjbq7u2XbtkpLS7V8+XLPXcSVOmcc4pwxAMBfho3xhAkT9Pjjj2djLEck1ufIkhQO2bLZ9AMA4CM5swNXrDehcCggy7JkWZYcNv0AAPhE7sR4YF9qiZkxAMBXcifGvQnlhZI/jt1/OpvzxgAAP8idGPfvSy0lZ8aSFGetGgDgA7kV44Fl6v4rvRMJZsYAAO/LnRj37jcz7o8xy9QAAD/IiRjHE44SjrvfBVzJzyeIMQDAB3IixrHejzf8kPZbpuacMQDAB3Ijxn0HxdjmnDEAwD9yIsb770stfTwzjrNMDQDwgZyI8VAzYy7gAgD4QU7EeGA5OhRI/jgD97BIJDhnDADwvpyI8cDWl1b/T5M6Z8zMGADgAzkR44FtqAfOFX98NTUxBgB4X47EuH9m3L88TYwBAH6SEzFOLVMPzIxTm35wzhgA4H05EeNDlql5nzEAwEdyJMYsUwMA/CsnYjzQXOvgC7iYGQMAfCAnYjwwM7YHZsY2e1MDAPwjR2Kc/HNgZpza9INlagCAD+REjB3XTQVYYtMPAIC/5ESMXddNnSeWOGcMAPCXHImxhpgZc84YAOB9ORFjZ6iZMcvUAAAfyIkYJ2fG+8U4tQMXMQYAeF+OxPigC7g4ZwwA8JGciLHj6oBlasuyZElyOGcMAPCBnIix6xw4M5aSF3GxTA0A8IOciHHyfcYH1ti2LMVZpgYA+EBOxNh1P94Kc4BlcwEXAMAfciTGriz70Jkx54wBAH6QEzF2XOmgiTHnjAEAvpETMXZdN7Xr1gDbsnhrEwDAF3Ikxjr0Ai5mxgAAn8iJGDuue+gytSXFOWcMAPCBnIix6+rQZWrbksMyNQDAB3IkxoNs+mGxTA0A8IeciPHBd22SkueQiTEAwA9yIsYH389YSt65ifsZAwD8IEdiPPh2mMyMAQB+kBMxPviuTVL/W5u4gAsA4AM5EWMu4AIA+FlOxNgZctMPzhkDALwvJ2Lsuu4hd22yLbFMDQDwhRyJ8SAzY5apAQA+kRMxdgY5Z2yxNzUAwCdyIsbuIJt+BGxLDjEGAPhAjsR48GXqeIILuAAA3pcTMR50mdoSy9QAAF/IiRi7zhCbfhBjAIAP5EaMNfimH47jynUJMgDA23Iixs4QM2OJpWoAgPflRIwH3w4z+ScxBgB4ne9j7LquXA2+HaYkJbiiGgDgcTkQ4+Sftn3oW5skKc7MGADgcTkQ42RsD1qlTsWY/akBAF7n+xgPTHytg2bGVuoCLpapAQDe5vsYD8yMB7trk8QFXAAA7/N9jFMz4yEv4CLGAABv832MU+eMB9n0Q2JmDADwvhyIcfLPoTb94GYRAACv832MndTMePC3NjEzBgB4ne9jPOQFXP0/GZt+AAC8LgdinPyTmTEAwK98H2NnqAu4uFEEAMAnfB/jIS/gYgcuAIBP+D7GQ8+Mk39yNTUAwOt8H+OhZsYW54wBAD6RAzEe7q1NzIwBAN4WHO4L9u7dq+985zv66KOPFA6HNX36dN18882qqKjIxviGNewFXJwzBgB43LAzY8uy9M1vflOrV6/WypUrNXXqVN11113ZGFtahr2Ai2VqAIDHDRvjsrIyLViwIPXxSSedpF27do3poDIx5N7UA5t+EGMAgMcNu0y9P8dx9Ktf/Up1dXUZvUhlZVFGX5+uxj1dys8LSZIKI3kqLspPPRbuS0iS8gvCikaLx+T1xxuOY3ZxvLOHY509HOvBZRTjW265RZFIRF/72tcyepGWlg45YzFDDQTU2dUrSerp6VV7RyD10MBbmlrbutXU1D76rz3ORKPFHMcs4nhnD8c6e8bDsbZta0QT0LRjvHTpUm3btk3Lly+XbXvnIuyBxA95NTXvMwYAeFxaMV62bJnWr1+vBx98UOFweKzHlJGBGffBvx8MtJlzxgAArxs2xps2bdIDDzygo446Sv/4j/8oSZoyZYruv//+MR9cOlIXcOnQTT9s2yLGAADPGzbGxx9/vD744INsjGVEhrprkyQFbYv3GQMAPM87J39HaGDTj8FOYwcCluLswAUA8Djfxzg1M9ahM+OAbTMzBgB4nu9jnNoOc5CfJHnOmJkxAMDbfB/jobbDlKQA54wBAD6QAzEefDtMqT/GXE0NAPA438fYGeIWipIUDNiKE2MAgMf5PsaHW6a2bYsduAAAnpcDMWaZGgDgb76PsXOYTT8CAWbGAADv832MB2bGNjNjAIBP5UCMk38OOjO2uYALAOB9vo+x47qDzoqlgWVqYgwA8Dbfx9h13UFnxdLAMjXnjAEA3pYDMR78Smqpf29qlqkBAB7n+xgnl6mHnhnHWaYGAHic72OcnBkfZpmatzYBADzO9zF2HHfoZeoAb20CAHif72PsavCtMCXOGQMA/MH/MR5uZswyNQDA43wfY2fYtzYxMwYAeJvvY+y6g2+FKXE1NQDAH3Igxq6sIWrMph8AAD/wfYwdVxpiYizbtuW6yaVsAAC8yvcxdl1X9lAz40Dy8+xPDQDwshyI8dCbfgT7I81SNQDAy3wf48PetclO/nhcUQ0A8DLfx/hwM2ObZWoAgA/kQIwPs+lH/5Q5zsYfAAAP832Mh7trk8QyNQDA23wf48Pfz5gYAwC8z/cxPtx2mMFA/wVcLFMDADzM9zFOboc5xAVczIwBAD6QAzEe/gIuYgwA8DLfx9g5zFubBnbg4mpqAICX+T7GbjqbfvA+YwCAh+VAjA8zM2aZGgDgA76PsXO4c8YB9qYGAHif72PsHnbTD5apAQDelwMxZpkaAOBvvo/x4e/a1H81NcvUAAAP832MXSeNmTHL1AAAD/N/jJXOBVzEGADgXb6PseMMvR3mxxdwsUwNAPAu38f4sNthDuzAxcwYAOBhvo6x67pyxTljAIC/+TrGjpuMrD3E5dSpZWqupgYAeJi/Y9y//DzsXZuYGQMAPMzXMU6kYjz0/YwtST19iSyOCgCAzPg6xgMz46E2/ZCkGdPKtOaNHdq2uz1LowIAIDM5EeOhZsaS9K1zPqmigpB+tOJt7euIZWtoAACkzdcxTgxzzliSSgvD+ufz56izp0/3rnhHvSxZAwA8xtcxTl1NfbgaS5pWXazL/mGWtta3acWLW7IxNAAA0ubvGKexTD3g5Nqojp9Sqm0NnDsGAHhLTsT4cBdw7S+SF1RPLD6GIwIAIHO+jvFwb206WEFeUD29nDMGAHiLr2M8cM44zRYrPxxQdy8zYwCAt/g7xk56F3ANyM8LqjvGzBgA4C2+jnE6b23aX0E4oHjCUZxbKgIAPMTXMXYznRmHg5LEeWMAgKf4OsYJN7MLuPLzApKkbq6oBgB4iK9jPNxdmw5WwMwYAOBBvo5xIuMLuJgZAwC8J2h6AEdiuJmxZVvq3D+8/V+4r7NXnbG48kJBBX396wgAIBfkRIyHmhnH+hJ6a2NT6uOBuza9u7VFXT19OmVmtYJ5vj4EAIAc4Ot5YaZvbQoFkj9uX5y3NgEAvMPXMXYzvJo6FCTGAADv8XWMUxdwpflTBIkxAMCDfB3jTG6hKCXPLQcDFjEGAHiKr2OcOmesNE8aK7lU3cd2mAAAD/F1jAfu2pTuMrUkhYIBZsYAAE8ZNmNLly5VXV2dZsyYoY0bN2ZjTGnLdJlaSl5RTYwBAF4ybIwXLlyoxx57TJMnT87GeDKS6VubpP5l6jjbYQIAvGPYHS/mz5+fjXGMSKZ3bZKSMe7pYjtMAIB3ZGX7qcrKojH5vgMz4+KifBUMspNWKBRUcVH+AZ+L5Ie0ryOm4qJ8RSJ5ilZExmRsuSgaLTY9hHGF4509HOvs4VgPLisxbmnpSJ3fHU0DF3B1dcUU7zt0ttvXF1d7R89Bn3UV60uovaNHXV0xNSVYsk5HNFqspqZ208MYNzje2cOxzp7xcKxt2xrRBNTXV1MnRnIBVzB5AdfA7l0AAJjm6xhnej9jKXk1tet+HHIAAEwbNsa33nqrPvvZz2r37t265JJLdPbZZ2djXGkZ7q5Ng2F/agCA1wx7zviGG27QDTfckI2xZGykb22SiDEAwDt8vUyd6V2bJGIMAPAeX8c44biyM5gVS/vFmP2pAQAe4esYO46b0axYSu5NLTEzBgB4h69jnHDcjM4XS1KYZWoAgMf4OsaO62Z0JbUkBQPEGADgLf6O8YiWqQdizM5bAABvyIEYZ/acYMCSJWbGAADv8H2MM12mtixLwaDN1dQAAM/wdYwTbuYzY+nj/akBAPACX8fYcVzZmb7RWMkrqokxAMArfB3jhONqBBNjBQPEGADgHb6OseO4skYwM2aZGgDgJb6PcaYXcEksUwMAvMX3MR7JBVxcTQ0A8BJ/x9jNfNMPSQoHA8yMAQCe4esYj+SuTdLH54wHbsEIAIBJvo7xSLbDlJLL1JLU28fsGABgnr9jPMJNP8L9N4vo6Y2P8ogAAMicv2M8wqupB24W0d3LzSIAAOb5OsaJES5TD8Q4RowBAB7g6xiP9K1NAzFmmRoA4AW+j/GRLFP3MDMGAHiAr2N8JHdtkogxAMAbfB3jI58Zs0wNADDP9zFmZgwA8LsciHHmNQ7YtmyLq6kBAN7g6xgnRrhMLUmhYIBlagCAJ/g6xiNdppaSS9U9MWbGAADz/B3jEd61SeqPcR8xBgCY5+sYj/SuTVJ/jFmmBgB4gK9jPNILuCSWqQEA3hE0PYAjMdK7NklSKGBrX0dML6zbqY3b96mju0//9L9nKy8cGN1BAgBBBE3SAAAX4klEQVQwDH/H+Aiupg6HAtrX0atHV3+gwvygOnvievfDPTq5NjrKowQA4PB8HeOE48oa4UnjTx5ToQUnVOuEo8pVWZKva370kt76WzMxBgBknW9j7LquXFca4Sq1igpCOmVmtQrzkofgk0dX6u0tLXLckc+2AQAYCd9ewOW4riTJHunl1Ac58bhKtXb06qOG9lH5fgAApMu3M2PHScb4SCaxlm2pM5Z8e9OxU8pkSVq7oUnR8ogkKS8UVNC3v64AAPzCtzFO9Mf4SJaUY30JvbWxKfXxhLJ8/fW9BkXL8iVJp8ysVjDPt4cIAOATvp33OU7yz9E8vTslWqSWth519bAZCAAge/wbY3dgmXr0ajylqlCStLO5c9S+JwAAw/FtjD9eph6971lWlKdIflA7mzpG75sCADAM354Q/fgCrtGrsWVZmhIt0pZdrdq6q00ffLRPH9a36eTaqM474xgF7I9/d9m4fZ/e37ZXkfygivJDipYV6NjJJaM6HgDA+ECMDzKlqlAbt+/TS2/XK5IX1JRooZ79y0f6qKFd3zrnkwoFbf3mhc36n9d3HPLcebVRfe2sGSotDI/qmAAAuc23MU64o79MLUmTJxTqtNkTVVIY1t8tmK7igpBeemuXfvHcB7rl52tlW5Ya9nbrC/OmaMnpx6gr1qfOnrje+luzfvfqh9rw0F5d8PnjNP8TVbw1CgCQFt/GeKxmxpZl6djJpZKkQNBWZyyukz9RpfKSfD38zHsKBixdfcEc1U4tk+NK7324R5JUVhTWov81XX96Z7d+/uwG7Wjs0LmfPYa3RgEAhuXbUozGph/DOfh9yIs/PV2WJbV2xLT2/QadeNA+1mVFeTprwTT95g+btXln69gNDACQU3y7iOqMwqYfmQoG7AMu4hpMwLZ0VE2xtjd2qKeX9ysDAIbn2xgnsjAzHqmja0qUcFy9/bcW00MBAPiAb2OculGEB2scLctXUUFIr21oND0UAIAP+DfGY3QB12iwLEtH1xRrw0d71drZa3o4AACP822MvbxMLUlHTyqR60p/fb/B9FAAAB7n2xibuIArE2VFeZoSLdSf3yXGAIDD82+MXW/PjCVp/ieqtLW+TQ17ukwPBQDgYf6NscdnxpI07xNVsixpxYubU788AABwMN/GOHXO2MM/QVlRnr70ueP02gdNenzN30wPBwDgUf7fgUvenRlbtqXTT6xRw94uPbd2uwojIdWdPCX1OHtXAwAkP8d44H3GHo7ZwHaaU6uLNK26SE++uEUf7mrTsZNLFckP6pSZ1exdDQDwb4wTHn6f8cFsy9Lpc2r0wrqdWrepWes2NauqvEDBgK3TZ9eYHh4AwDDfxtgPF3DtLxiw9YX5U7WvI6Ztu9u1tb5dP/3d+5pYHtFxU0pNDw8AYJCHF3kPzw9vbRpMWVGeTjxughb9r2kqL87TQ8+8q+4YN5QAgPHMtzH20zL1YMLBgC7+u0+oeV+P/t+aTaaHAwAwyLcx/niZ2vBAjsCxk0v195+arj++Va91m5oOebw7FtfO5k7FE46B0QEAssVX54ybW7tlyVJlab6nbxSRiXM/c7TWb23RT556V+XFYUXyQgoFbTW3dmtfR/ImE0fXFOtbS2apqjxieLQAgLHgqxj/+g+b1bC3Szddcqr6W+y7c8b7s2xLsbijr599gl5Yt1Md3X3qjsXVG0+odlq5Jk8oVH44oN++vFU3/XStLjprhk6ujWp7Q4e27m5TXiigT51QrXAoYPpHAQAcAV/F+NhJJVq7oVGNe7tS54z9cjX1YAbehyxJ06qLDnn81FkT5TquPjG9XD9/doMeWvmeLEvaf2fNJ17crC+eMlWfnztFkfzD/+OMJxy5rqtQkHgDgJf4KsYnz4jq/675m17/oCm1PO33ZerD2T/Wn/7kRFWW5KunN67K0nxNKM3X5GiR/uf1HVrx4hb9918+0vlnHKvPnjRp0F9QNu9s1T2/eVu98YTmHFOpk2ujmnPshGEDDgAYe776L/GE0gJNn1is1zc26eTaqCR/L1NnwrYtzTyq/IDPHT+tXMdNKdP2hnY9+cctenT1B3rxrV368sLjNbWqKLXd5rpNTXrg6XdVVpyn+UdVad3GJr32QZMCtqUTjqrQybUTVDu1TH1xR109cfX0JpIvYCUvkMsLBdQaS6inM6bK0nwFA7697g8APMlXMZak+TOiWvHiFk2rLpbk72XqI7X/zPlTs6pVXRHRaxsadcdjb6i0MKxZx1SqMD+o37+2XVOrinX5ubNUHAnrf59xjD6sb9O7W/Zq3aZG/fy/W9J+zbxQQDOmlemEoyp04nGVquaiMgA4Yr6L8cm1yRi/8UGjpPEzMx6OZVk6ZlKJpkQLtXFHqxr2dGndxkZ1xxKaHC3UabMnasO2vQc8Z8lnj9bi06ZrZ3OndjZ2KD8vqIK8oPJDAcmSAnZAvfG4evsSci1brW3d2tbQrg8+2qe3N7fo//7PJtVOLdNn5tRo5vRy7Wru1LaGdjW39mhCab4mVRaqZkKhomX5Cnh5E/EMuK6rLfVteuODJpUUhlV38mTOwQM+s6u5UxMrIrI99N5Y38W4prJQkycUamdzpyzl9jnjkQiHAvrk0RX65NEVmn38BL36dr0K84ODHqf9Z9a2bam3L6HevoRa+x8/sTaaery4KF+2benomhIdXVOiju4+xROuXl1fr0dWvX/A943kB9XV8/GuYsGApeqKiCZVFuqk4yfo1JlVvopzX9zRxh37tH5Li17b0KSWth4FbEsJx9XvX9uu8z93rBbMrFZ3LKHm1m7FE66Orinm303Ag/741i797NkNOn12jS5Z9AnP/D1NK8Zbt27V9773Pe3bt09lZWVaunSpjjrqqDEe2tBOro1qZ3Onp36r8SLbslRUEBqT711UENKJtVGVFobUsLdb+9pjKivKU3lJnvJCAfX2JTSlulj1zZ3a3dKlhj1d+tvOVq3d0Kgn/7hFZ546VbOPjaq9M6Y9bT1yHFe1U8tUUZI/JuNNR1dPn7Y3dmhXS5daO2La29Grpn3d2rKzVb1xRwHb0ozp5fr7T03T7GMnaPeebv36D5v04G/f08+e3aDevo83Zzl2con+T12tjp9cMuzruq6r9u4+Ne7tVuPeLrV39WlSdYls11FZYVilRXkH/ELluq66YnFtb+jQph37tGlnqxzH1dzjozq5Nqry4rwxO0aAX7z5t2Zt3tmqRZ+aroL+u+Nt2LZXv1j9gcqKwnr5nXpNmlCov1swzfBIk9KK8Y033qgLL7xQ55xzjp5++mn94Ac/0KOPPjrWYxvSvBlRrfzThwoQY+Msy9LEiogmVhx47jgcCqhmQqEa93RpcrRQk6OFmls7QdsbO/TO5hb98vlN0vOHbgNaXV6g46aUKhSw5UqyJJUW5WlC/xXkccdVa0dMrR29ivUlZFuWLNuSbSV/+bAsS5aV3C41nnDUF3e0rz2mPe0x7euIqSAvqOryAlWVR2RbUktbTC1tPWrY06Xm1p6Pfy5JRZGQggFbR08q0eQJhaquiCjUfwPqd7e26MTaqD4/d5K27GpTc2uPCgtCKi4IqTsW19ubW/Qfv3hN82qjqizNVzzhKJ5wlR8OqKQwrOKCkPZ19mrrrjZtqW9TW2fvYY9zMGCpOBJOXWTn7Pf+tprKiFxXeuz5jfrl8xt1VE2JavvP69dOKZUlqacvoVhvQgHbUl44oLxQgAvx4Auu66q+pUsfNbTruMmlmlBWICl5f4L3PtyjP63frbLCPJ1x0iRVV0TU1RPXr/5no155Z7ck6c/vNugbZ89URUme7n/yHVWVF+j6i+bp589u0K//8DdNrIzopOMmmPwRJaUR45aWFr333nv66U9/KklavHixbrnlFu3Zs0cVFRVpvchoz2CnTyzWJ6aXqaM7rkj+0DO/YMDm8VF6vCAvqEQ8NOTj6X7/T0yv0Ixp5Wrc162KknyVRMIqLQrLcVx9WJ8M067mLrmuK0uWHNdV10HnujNhW1JRJKySwrCqKiLq6Y2ruS2mLbva5EoqLAiptDBPc46doOrKAtVUFqqqPKKigpAsy9I7m5sP+/MVFoQ1+9hD/yLPOW6CWjt79dd3G9Swr1tB25Jt24r1xdUX/3gGXVmarwUnVKumMqKKknxVlOSrsCCohGupta1Hezt61NkVV1t3nzq7+xQMWIr0n9uvKM1Xe2dvatOXtq6YdjV1qr6lS29uatK6jYdusbq/gG0pFAwoFLJVEA6ouCCkokhY+eGAYn2Oenrj6utLKD8vqEheUJH8kHrjCXX1xNXV0yfHcRWwLdkBO/mnZcm2reT/ty0FbUuyLHXH4uqOJa/Szw/ZKoqEVVwQliNX3bE+dccScpzk+9/DITv5S4LrytXAe+rd1HvrA/2vFbRtxR0neWol7si2kr9k5IcCCgYtOU5yy9yE6yb/dNz+17CTv4wEbTmuq94+V1bQUldXn6TkL2G2ZSkYtBUKJl+nN+Eo1pdQvH91JBQIKBDs/2+am3wdx3XluMlwSFIoaCsUtBXsP50RT7hKJFzFHUeJhKO44yoUsFNjtiwpFk/+8ug4rmzbkq3kcbTs5D8ryxp4LclJfPyzDXx9XiigUNCWbUk9vf3//BJO6jXCoYCc/l9SE/3PCdqWAgFbCcdRdyyhnlhcrlzlhYMqCAcUtG119ybUHetTrM9RXiigSF5Q+eGAehOOemJxdccSsm2l/j2xJHV2x9Xe3ad4wlEkP6Ci/LDywgHFJTU2daqtp08F4YBKImGVFIbU3ZPQ7j2datjbLcdxVV0RUXVFRAHb0vvb9mhPWyz17+3EioiOrinRB9v3ak9bTJG8oLbtbtcbm5p0/JRSNe/r0b7OmP7xC8erdkqpfv3CZv3svzeoqCCkaFmBrj5/toojYV12zif1k6fW64k/btHkaJGqywsO+/clXSPtneW6+28hcaj169fru9/9rlatWpX63KJFi3TnnXdq1qxZI3pRAADwMdapAAAwbNgY19TUqKGhQYlEciOIRCKhxsZG1dTUjPngAAAYD4aNcWVlpWbOnKlnnnlGkvTMM89o5syZaZ8vBgAAhzfsOWNJ2rx5s773ve+pra1NJSUlWrp0qY455phsjA8AgJyXVowBAMDY4QIuAAAMI8YAABhGjAEAMIwYAwBgmC9jvHXrVn35y1/WWWedpS9/+cv68MMPTQ8pZy1dulR1dXWaMWOGNm7caHo4OW3v3r269NJLddZZZ+kf/uEfdNVVV2nPnj2mh5WzrrzySi1ZskTnnnuuLrzwQr3//vvDPwlH5L777uO/JUPwZYwHblyxevVqXXjhhfrBD35gekg5a+HChXrsscc0efJk00PJeZZl6Zvf/KZWr16tlStXaurUqbrrrrtMDytnLV26VL/97W/11FNP6etf/7quv/5600PKae+++67efPNN/lsyBN/FeODGFYsXL5aUvHHFe++9xwxijMyfP5/d1rKkrKxMCxYsSH180kknadeuXQZHlNuKi4tT/7+jo8Mz97XNRb29vbr55pt10003mR6KZ6V1C0Uvqa+vV3V1tQKB5J1qAoGAqqqqVF9fz65gyBmO4+hXv/qV6urqTA8lp33/+9/XK6+8Itd19fDDD5seTs665557tGTJEk2ZMsX0UDzLdzNjYDy45ZZbFIlE9LWvfc30UHLabbfdphdeeEH/8i//ojvuuMP0cHLSunXrtH79el144YWmh+JpvosxN65Arlu6dKm2bdumu+++W7btu7+ivnTuuefqL3/5i/buHfm9szG4tWvXavPmzVq4cKHq6uq0e/dufeMb39DLL79semie4ru/6dy4Arls2bJlWr9+ve6//36Fw2HTw8lZnZ2dqq+vT328Zs0alZaWqqyszOCoctNll12ml19+WWvWrNGaNWs0ceJEPfLIIzr99NNND81TfLk3NTeuyJ5bb71Vzz33nJqbm1VeXq6ysjKtWrXK9LBy0qZNm7R48WIdddRRys/PlyRNmTJF999/v+GR5Z7m5mZdeeWV6u7ulm3bKi0t1Xe/+13NmjXL9NByXl1dnZYvX67a2lrTQ/EUX8YYAIBc4rtlagAAcg0xBgDAMGIMAIBhxBgAAMN8twMXAACjaenSpVq9erV27typlStXDnul9xtvvKEf/vCHqY9bWloUjUb15JNPjngMzIyBHHPRRRfp17/+9Yieu2vXLs2dOze1qQ4wHmR6Q5yTTz5ZTz/9dOp/c+bMSd0vYaSIMTCO1dXV6U9/+lPq40mTJmndunWpvd+B8WCoG+K89dZbuuiii3TeeefpvPPO0wsvvHDI17S0tOiVV17ROeecc0RjYJkaAICDtLW16cYbb9SDDz6oqqoqNTY26oILLtAzzzyjkpKS1Nc99dRTOu200zRhwoQjej1mxsAYqqur0wMPPKBFixbplFNO0b/9278pFotJkh5//HF98Ytf1KmnnqrLL79cDQ0NqefNmDFDjz76qBYuXKgFCxZo6dKlchxHknTvvffquuuuS33tjh07NGPGDMXj8UNe/6OPPtLFF1+sBQsWaMGCBfr2t7+ttrY2SdK//uu/ateuXbr88ss1d+5cPfTQQ4d8r4aGBl1++eU69dRT9cUvflGPP/546nvfe++9uuaaa/Sd73xHc+fO1dlnn6133nln9A8iYMC6deu0Y8cOXXrppTrnnHN06aWXyrIsbdu27YCve+KJJ3T++ecf8esRY2CMrVy5Uo888oief/55bd26VT/+8Y/16quv6j//8z9199136+WXX9bkyZN17bXXHvC8559/XitWrNCTTz6pNWvWaMWKFRm/tuu6+ta3vqWXXnpJzz77rHbv3q17771XknTnnXdq0qRJWr58udatW6dLL730kOdfe+21mjhxol566SX96Ec/0rJly/Tqq6+mHl+zZo3OPvtsvfbaa6qrq9Mtt9yS8RgBL3JdVzNmzDjg3PCLL76o2bNnp77mzTffVGtrq84444wjfj1iDIyxr371q6qpqVFZWZmuuOIKrVq1SitXrtT555+vWbNmKRwO69prr9Wbb76pHTt2pJ536aWXqqysTJMmTdLFF1+cujlKJqZPn67TTjtN4XBYFRUVuuSSS7R27dq0nltfX6833nhD1113nfLy8jRz5kx96Utf0tNPP536mnnz5umMM85QIBDQOeecow0bNmQ8RsCL5s6dq23btunPf/5z6nNvv/229t9BesWKFVqyZImCwSM/48s5Y2CM7X9hyKRJk9TY2KjGxsYDbkpQWFiosrIyNTQ0pG7Avv/zJk+erMbGxoxfu7m5Wbfddptee+01dXZ2ynXdA853HU5jY6NKS0tVVFR0wPjXr1+f+nj/82T5+fmKxWKKx+Oj8h8nIFv2vyHOJZdckrohzo9//GPdeeeduv3229XX16epU6dq+fLlsixLPT09+t3vfnfAqZsjwd8YYIztf6u+Xbt2qaqqSlVVVdq5c2fq811dXdq3b5+qq6sPeN7xxx9/wPMkqaCgQD09Pamva25uHvK1ly1bJsuytHLlSpWVlen3v/+9br755rTGXVVVpdbWVnV0dKSCXF9ff8AYgVxwww036IYbbjjk83PmzNEvfvGLQZ+Tn5+v119/fdTGwDI1MMZ++ctfavfu3dq3b5+WL1+uRYsWafHixXriiSf0/vvvq7e3V8uWLdOcOXNSs2JJeuSRR9Ta2qr6+no9+uijWrRokSRp5syZWrt2rXbt2qX29nY98MADQ752Z2enIpGIiouL1dDQoIcffviAxydMmKDt27cP+tyamhrNnTtXy5YtUywW04YNG/Sb3/xGS5YsGYWjAmB/xBgYY4sXL9bXv/51feELX9C0adN0xRVX6NOf/rSuueYaXX311Tr99NO1fft2/dd//dcBz1u4cKHOO+88nXvuufrc5z6nCy64QJJ02mmnadGiRVqyZInOO+88ff7znx/yta+66iq99957mj9/vi677DKdeeaZBzx+2WWX6Sc/+Ynmz5+vRx555JDnL1u2TDt37tRnPvMZXXXVVbr66qv16U9/ehSOCoD9cT9jYAzV1dXp1ltvzThgM2bM0HPPPafp06eP0cgAeAkzYwAADCPGAAAYxjI1AACGMTMGAMAwYgwAgGHEGAAAw4gxAACGEWMAAAwjxgAAGPb/AbD8SErZVgvgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_data = data['population'].dropna()\n",
    "ax = sns.distplot(dist_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "ee8745027b7023ebfe1a26da2abd532ec939fc45"
   },
   "source": [
    "### 4.4 Scatterplot and Hexplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "e4fa89aae0843212f1ceb64df56224e627809030"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.regplot(x=albania_data.population, y=albania_data['suicides_no'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "3c2448fd552dd2ec3f0189f13a8937a85c33d1c4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.jointplot(x=albania_data.population, y=albania_data['suicides_no'],kind='hex',gridsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a1b60bbbe7430deb60bd4a6539aa24e0fc60cf98"
   },
   "source": [
    "### 4.5 Boxplot and Violin plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "61986b7f9d498df2dc08b8dc2f8cbf51afaf9112"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.boxplot(x=albania_data.population, y=albania_data['suicides_no'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "b4d0ae24214d1515e2eb2ac413032e0bf06ec8bb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2639cdd0b8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=albania_data.population, y=albania_data['suicides_no'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8bdafd8ff5ffa99993b0f9e79ef2b37acb111ee0"
   },
   "source": [
    "## 5.Scikit-learn \n",
    "Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.\n",
    "![Imgur](https://i.imgur.com/dlkY8VU.png)\n",
    "The scikit-learn project started as scikits.learn, a Google Summer of Code project by David Cournapeau. Its name stems from the notion that it is a \"SciKit\" (SciPy Toolkit), a separately-developed and distributed third-party extension to SciPy. The original codebase was later rewritten by other developers. In 2010 Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort and Vincent Michel, all from INRIA took leadership of the project and made the first public release on February the 1st 2010.Of the various scikits, scikit-learn as well as scikit-image were described as \"well-maintained and popular\" in November 2012."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6572d3a4e08943a69d4749e88f6dc815e8757c9c"
   },
   "source": [
    "## > **Coding section very quickly upcoming.....**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
